数据采集与处理2025，Vol.40Issue(1)：117-133,17.DOI:10.16337/j.1004-9037.2025.01.009
混合层次依赖度下的邻域粗糙集多目标特征选择算法
Multi-objective Feature Selection Algorithm for Neighborhood Rough Set Under Mixed Hierarchical Dependence
摘要
Abstract
Accuracy and efficiency are the key metrics for evaluating the performance of feature selection algorithms.They correspond to the attribute dependence and reduction scale of neighborhood rough sets respectively.Conventional feature selection algorithms often optimize solely based on maximum attribute dependence reduction,overlooking the significance of reduction scale.However,as data feature dimensions increase and category hierarchies emerge,category information becomes complex and structural relationships become chaotic.Traditional attribute dependency calculations fail to effectively utilize category hierarchy information,leading to suboptimal classification performance.In response to this,a mixed hierarchical dependency that considers the relationship between attribute importance and category hierarchy structure is constructed.This treats mixed hierarchical dependency and reduction scale as two independent optimization objectives,and introduces a multi-objective evolutionary algorithm to optimize them independently.This approach improves attribute reduction performance from both the attribute dependency and attribute scale perspectives,resulting in reduction results that meet target constraints.Experimental results demonstrate that the proposed algorithm achieves higher-quality reduction results within target constraints,leading to the improvement of classification accuracy.关键词
多目标特征选择/邻域粗糙集/层次结构/混合层次依赖度/属性约简Key words
multi-objective feature selection/neighborhood rough set/hierarchical structure/mixed hierarchical dependence/attribute reduction分类
信息技术与安全科学引用本文复制引用
骆公志,张尚蕾..混合层次依赖度下的邻域粗糙集多目标特征选择算法[J].数据采集与处理,2025,40(1):117-133,17.基金项目
国家自然科学基金(72171124) （72171124）
江苏高校哲学社会科学研究重大项目(2021SJZDA129) （2021SJZDA129）
江苏省研究生科研创新计划项目(KYCX22_0884). （KYCX22_0884）
